1. A TrendLabsSM
Research Paper
Follow the Data:
Analyzing Breaches by Industry
Trend Micro Analysis of Privacy Rights Clearinghouse
2005–2015 Data Breach Records
Numaan Huq
Forward-Looking Threat Research (FTR) Team

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Contents
Overview
4
Healthcare
10
Government
12
Retail
15
Finance
17
Education
20

3. Our data source
The Privacy Rights Clearinghouse (PRC) is a nonprofit corporation based in
California. PRC’s mission is to engage, educate, and empower individuals
to protect their privacy1
. They do this by raising consumers’ awareness of
how technology affects personal privacy, and they empower consumers
to take actions to control their personal information by providing practical
tips on privacy protection. PRC responds to privacy-related complaints
from consumers and where appropriate intercedes on the consumer’s
behalf/refers them to the proper organizations for further assistance. PRC
documents consumers’ complaints and questions about privacy in reports
and makes them available to policy makers, industry representatives,
consumer advocates, media, etc. PRC advocates consumers’ privacy
rights in local, state, and federal public policy proceedings.
PRC publishes the “Chronology of Data Breaches Security Breaches
2005–Present1
,” which is a collection of publicly disclosed data breach
incident reports that occurred in the United States. The data is compiled
from a variety of sources including: media, Attorney General’s Office press
releases, company press releases, privacy websites, etc.
All data breach incident reports in this paper have been collected from the PRC
database for the period, January 2005–April 20152
. PRC’s original “Organization
Types” were expanded to include a wide range of industries in order to provide
a fine-grained view into victim profiles. Each entry was analyzed to determine
the record types compromised. The data collected was analyzed using tools that
include KH Coder3
, MSBNx4
, and Explore Analytics5
.

4. 4 | Follow the Data
Hacking or malware
Insider leak
Payment card fraud
Physical device loss
Portable device
Stationary device
Unintended disclosure
Unknown
800
400
0
2014 20152005 2006 2007 2008 2009 2010 2011 2012 2013
Further analysis of publicly disclosed
data breach incidents
This material supplements the paper, “Follow the Data: Dissecting Data Breaches and Debunking Myths,”
where we looked at the volume of incidents over a span of 10 years and formulated a Bayesian model in
order to determine the probability that different breach scenarios would occur. We also took a closer look
at where stolen data eventually ended up and what interested parties could do with it.
Here, we will take a closer look at the different breach methods, record-type combinations stolen, and
industry cross-sections of the PRC data.
Figure 1: Breach methods observed from 2005 to April 2015
Figure 1 shows an interesting visualization that emerged when breach methods were plotted on
a timescale.
• Portable device (USB keys, portable drives, laptops, etc.) loss or theft is a major contributing factor in
the leakage of sensitive data. This remains a constant threat over the years.

5. 5 | Follow the Data
Healthcare
Education
Government
Retail
Financial
Service
Banking
Technology
Insurance
Media
Others
26.9%
16.8%
15.9%
12.5%
9.2%
3.5%
2.8%
2.6%
1.6%
1.4%
6.8%
• The number of hacking or malware attacks steadily rose from 2010 onward. As the Internet expands
and new applications are introduced, businesses are steadily growing their online presence, leading
to an increase in hacking or malware attacks against them.
• A big increase in the number of insider threats was seen from 2010 onward. There are two plausible
explanations for this. First, insider threats have always been present and not properly reported.
Second, insiders now find it monetarily lucrative to steal and sell sensitive data, and so, commit more
crimes.
Figure 2: Industries affected by data breaches
Any business or organization that processes and/or stores sensitive data is a potential breach target.
Figure 2 shows that the healthcare, education, government, retail, and financial industries were the most
frequent victims of data breach crimes. The PRC data shows that these five industries accounted for
81.3% of the total number of disclosed data breach incidents. This is not really surprising, as businesses
or organizations that belong to these industries are a treasure trove of valuable sensitive data and so
would provide good returns on investment (ROIs) for the cybercriminals that compromise them.
All of the aforementioned industries are subject to specialized state and federal data breach disclosure
laws. That’s why we see them dominate incident reports. Other industries are also targeted but incident
reports for them are few likely because of:
• Failure to identify data breaches
• Failure to properly assess data breaches
• Noncompliance with data breach notification laws

6. 6 | Follow the Data
PII
Health + PII
Financial + PII
Health
Payment card
Credentials + PII
Education + PII
Financial + health + PII
PII + payment card
Financial
Others
34.0%
12.8%
8.3%
5.8%
5.2%
3.9%
3.7%
3.5%
3.0%
2.8%
17.0%
• Lack of knowledge of data breach notification laws
• Nondisclosure because of active investigation by authorities
• Breaches did not lead to the compromise of data that legally mandates public disclosure
Often, organizations are not even aware of ongoing data breaches and require external parties (banks,
law enforcement agencies, customers, etc.) to inform them. Data breach news stories are regularly
leaked to the media. Media practitioners also have ongoing investigations or learn about breaches from
insiders. Large data breaches are typically followed by class-action lawsuits, which damage businesses
or organizations and may even deter them from immediately disclosing incidents.
Figure 3: Record-type combinations compromised
Figure 3 shows that a wide range of data gets stolen from businesses (big and small) and individuals,
including:
• Personally identifiable information (PII): Names, addresses, Social Security numbers, and other
information.
• Financial data: Banking, insurance, billing, and other information.
• Health data: Hospitals and doctors’ office records, medical insurance, and other information.
• Education data: School, college, university, or related records.
• Payment card data: Credit, debit, store-branded credit, and prepaid gift cards.
• Credentials: Log-in credentials to eBay, PayPal, Web-based emails, online banking sites, and others.
• Others: Intellectual property, intelligence about an organization, and other information.
• Unknown: In many cases, investigators failed to determine what was stolen.

7. 7 | Follow the Data
Hacking or malware
Insider leak
Payment card fraud
Physical loss
Portable device loss
Stationary device loss
Unintended disclosure
Unknown
2K
1K
0
PII Health
+ PII
Financial
+ PII
Health Payment
card
Credentials
+ PII
Education
+ PII
Financial
+ health
+ PII
PII +
payment
card
Financial
Figure 4: Top 10 record-type combinations compromised versus breach methods used
Figure 4 shows that the most popular record types stolen were PII; health, financial, education, and
payment card data; and credentials. These are easy to monetize and thus make lucrative targets. Health
and education data is mostly stolen because they contain PII. Data presented later will support this claim.
PII was most commonly compromised. The usual breach methods for PII theft were unintended disclosure,
loss or theft, insiders, and hacking or malware. No breach method could be singled out as the major
contributing factor for compromising PII; all available means were instead employed.

8. 8 | Follow the Data
Company name
Records
compromised
Disclosure date Record types compromised
eBay 145M 21 May 2014 Credentials, PII
Heartland Payment Systems 130M 20 January 2009 Payment card data
Target 110M 13 December 2013 PII (70M), payment card data (40M)
The Home Depot 109M 2 September 2014 PII (53M), payment card data (56M)
Sony PlayStation Network 101.6M 27 April 2011 Credentials, financial data, PII,
payment card data
TJ stores (TJX) 100M 17 January 2007 PII, payment card data
Anthem 80M 5 February 2015 Financial data, PII
JP Morgan Chase 76M 28 August 2014 Credentials, financial data, PII
Evernote 50M 3 March 2013 Credentials
Epsilon 50M 2 April 2011 PII
CardSystems 40M 16 June 2005 Payment card data
Adobe 38M 4 October 2013 Credentials, PII, payment card data
Steam (The Valve Corporation) 35M 10 November 2011 Credentials, PII, payment card data
RockYou 32M 15 December 2009 Credentials
LivingSocial 29M 26 April 2013 Credentials, PII
Zappos.com 24M 15 January 2012 Credentials, PII
WordPress 18M 14 April 2011 Credentials, other data
Countrywide Financial Corp. 17M 2 August 2008 Financial data, PII
DeviantArt 13M 17 December 2010 PII
Bank of New York Mellon 12.5M 26 March 2008 Financial data, PII
Figure 5: Top 20 publicly disclosed data breach incidents as of April 2015
The total number of records compromised is typically used to quantify the size and extent of a data
breach incident. Figure 5 shows that the biggest incidents were not concentrated in the past five years
but spread out across 2005 to April 2015.
The biggest payment card breach incident was not the one that involved Target or Home Depot but that
which affected Heartland Payment Systems in 2009. Albert Gonzalez was charged by the authorities for
this breach and sent to prison for 20 years6
. The biggest credential leak involved eBay in 2014. Hackers
compromised its employees’ log-in credentials, allowing them access to the corporate network and to
compromise the main databases that held all user passwords. The criminals also gained access to PII like
names, email addresses, home addresses, phone numbers, and dates of birth7
. The biggest data breach
to date involved Anthem where 80 million records were compromised and up to 18.8 million people were
affected. PII and employment information like income data was stolen8
.
Massive-scale data breaches are not that common, as they entail careful planning and prolonged effort on
the criminals’ part in order to succeed. Criminals tend to get better ROIs by targeting smaller businesses
and organizations, which have weaker defenses and so are easier to penetrate and compromise.

9. 9 | Follow the Data
Analysis of top 5 industries affected
by data breaches
The healthcare, education, government, retail, and financial industries are frequent data breach victims.
We studied five data sets for each industry and looked at trending patterns.
• Data breach incident disclosures: In this data set, we looked at incident numbers broken down by
year reported. Only a fraction of all of the incidents was actually reported. An increase in the number
of reported incidents strongly indicates that the total volume of data breaches has increased and vice
versa.
• Data breach methods: In this data set, we studied the breach methods used. Data breach incidents
could be due to hacking or malware attacks; insider threats; payment card fraud; physical loss or theft
of portable drives, laptops, office computers, files, and others; or unintended disclosures. In a small
number of cases, the actual breach method remains unknown or undisclosed.
• Top 3 data breach methods observed and trends from 2005 to 2014: In this data set, we looked
at the incident report numbers for the top 3 data breach methods broken down by year reported. A
logarithmic trend line was calculated for each breach method.
• Record types compromised: Criminals tend to steal all kinds of available data. Examples include PII
stored as part of health and education records. In some cases, financial data like billing, insurance,
and other information is stored along with health records. As such, these get stolen together. In this
data set, we looked at the record types and record-type combinations compromised.
• Probability of compromising different record types for each breach method: In this data set, we
looked at the probability of different record types getting compromised for each breach method.

10. 10 | Follow the Data
300
150
0
20142005 2006 2007 2008 2009 2010 2011 2012 2013
Portable device loss
Physical loss
Insider leak
Unintended disclosure
Stationary device loss
Hacking or malware
Payment card fraud
Unknown
33.7%
17.6%
17.5%
12.8%
8.7%
6.9%
0.1%
2.7%
Figure 6: Healthcare data breach incident disclosures from 2005 to 2014
Figure 7: Breach methods observed in the healthcare industry
The loss or theft of portable devices, backup drives, files, laptops, office computers, and other devices
accounted for almost two-thirds of all breaches. Surprisingly, hacking or malware attacks accounted for
less than 10% of all of the breaches.
Data breaches in the healthcare industry
The number of data breach incidents increased from 2010 onward. The “Health Insurance Portability and
Accountability Act (HIPAA),” which outlines patient data privacy requirements, has been around since
1996 and mandates that organizations in the healthcare industry disclose all data breaches. The upward
trend in incident report numbers suggests that the healthcare industry has recently become a lucrative
target.

11. 11 | Follow the Data
Health + PII
Health
PII
Financial + health + PII
Financial + PII
Others
40.3%
19.2%
14.9%
10.6%
1.8%
13.2%
Figure 8: Top 3 breach methods observed in the healthcare industry from 2005 to 2014
The top 3 breach methods in the healthcare industry were loss or theft, insider leaks, and unintended
disclosures. From the reported numbers, we observed a marked increase in loss or theft incidents from
2010 onward. A possible explanation for this increase, apart from more theft, was better incident reporting.
The trend line for each breach method shows an upward trend with loss or theft growing more than both
insider threats and unintended disclosures.
Figure 9: Record types compromised in the healthcare industry
The most popular record types and record-type combinations compromised were health and PII, PII,
health data, and PII and financial and health data. In some cases, patient files contained billing and
insurance information, leading to their theft.
150
75
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Loss or theft
Insider leak
Hacking or
malware

12. 12 | Follow the Data
50%
20%
0
Hacking or malware
Insider leak
Loss or theft
Unintended disclosure
Payment card fraud
Unknown
PII Health Financial Payment
card
Education Credentials Unknown Others
120
60
0
20142005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 10: Probability of compromising different record types
per breach method in the healthcare industry
PII and health data had the highest probability of getting compromised. There is a small chance that
financial data like billing, insurance, and other information will also get compromised. The most probable
breach method for stealing PII and health data is loss or theft.
Data breaches in the government sector
Data breaches follow a pattern that starts with a big increase in incident numbers reported in one year,
followed by several years of decline. Every time there is an increase in data breach incidents, new policies,
protocols, and procedures are most likely implemented, driving the incident numbers down.
Figure 11: Government sector data breach incident disclosures from 2005 to 2014

13. 13 | Follow the Data
Unintended disclosure
Portable device loss
Hacking or malware
Physical loss
Insider leak
Stationary device loss
Unknown
26.6%
24.0%
17.4%
14.6%
10.7%
3.5%
3.2%
80
40
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Loss or theft
Unintended
disclosure
Hacking or
malware
Figure 12: Breach methods observed in the government sector
Across government organizations, loss or theft of portable devices, backup drives, and others was the
biggest contributing factor in data breaches. Unintended disclosure of sensitive data through mistakes or
negligence is another major problem.
Figure 13: Top 3 breach methods observed in the
government sector from 2005 to 2014
The top 3 breach methods in the government sector were loss or theft, unintended disclosures, and
hacking or malware attacks. Based on the reported incident numbers, there was a gradual decline in loss
or theft incidents. This suggests the presence of strong policies, protocols, and procedures that help
reduce such incidents. The trend line for unintended disclosures shows that this threat remained fairly
consistent over the years whereas hacking or malware attacks increased.

14. 14 | Follow the Data
PII
Financial + PII
Health + PII
Financial
Credentials + PII
Others
57.4%
10.8%
6.6%
3.8%
3.4%
18.0%
40%
20%
0
Hacking or malware
Insider leak
Loss or theft
Unintended disclosure
Payment card fraud
Unknown
PII Health Financial Payment
card
Education Credentials Unknown Others
Figure 14: Record types compromised in the government sector
PII theft dominated the data breach incidents in the government sector.
Figure 15: Probability of compromising different record types
per breach method in the government sector
Among breaches that targeted government organizations, PII had the highest probability of getting
compromised. There is a small chance that financial and health data will also be compromised. The most
probable breach methods for stealing PII were loss or theft, unintended disclosures, hacking or malware
attacks, and insider threats.

15. 15 | Follow the Data
120
60
0
20142005 2006 2007 2008 2009 2010 2011 2012 2013
Hacking or malware
Portable device loss
Insider leak
Unintended disclosure
Physical loss
Payment card fraud
Stationary device loss
Unknown
47.6%
12.5%
12.2%
8.9%
6.8%
6.2%
2.9%
2.9%
Data breaches in the retail industry
Data breaches have become commonplace in the retail industry after the development of PoS RAM
scrapers sometime between 2007 and 20089,10
The upward trend observed in the incident report numbers
reflected this growing threat.
Figure 16: Retail industry data breach incident disclosures from 2005 to 2014
Figure 17: Breach methods observed in the retail industry
Hacking or malware attacks were common in the retail industry. PoS RAM scrapers were used to collect
payment card data while a variety of infiltration techniques were employed to gain initial entry into and
laterally move across target networks in order to compromise PoS servers. Insider threats mostly refer to
employees who use skimming devices to steal credit card data.

16. 16 | Follow the Data
60
30
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Hacking or
malware
Loss or theft
Insider leak
PII
Payment card
PII + payment card
Financial + payment card
Credentials + PII
Others
27.9%
24.9%
12.0%
5.9%
4.8%
24.5%
Figure 18: Top 3 breach methods observed in the retail industry from 2005 to 2014
The top 3 breach methods used in the retail industry were hacking or malware attacks, loss or theft, and
insider threats. The reported incident numbers show a big increase in hacking or malware attacks, which
could be attributed to PoS RAM scrapers. Insider threats showed an upward trend while the trend line for
loss or theft remained consistent over the years.
Figure 19: Record types compromised in the retail industry
The retail industry is a hotbed for the theft of payment card data, financial information, and PII and
payment card data.

17. 17 | Follow the Data
40%
20%
0
Hacking or malware
Insider leak
Loss or theft
Unintended disclosure
Payment card fraud
Unknown
PII Health Financial Payment
card
Education Credentials Unknown Others
80
40
0
20142005 2006 2007 2008 2009 2010 2011 2012 2013
Figure 20: Probability of compromising different record types
per breach method in the retail industry
Payment card data and PII had the highest probability of getting compromised. The most probable
method for stealing payment card data was hacking or malware attacks. For PII, the most probable
breach methods were hacking or malware attacks and loss or theft.
Data breaches in the financial industry
Similar to the pattern observed in the government sector, the financial industry posted a big increase in
incident numbers reported in one year, followed by several years of decline. It is probable that every time
the number of incidents increases, new policies, protocols, and procedures were implemented, which
drove the trend down.
Figure 21: Financial industry data breach incident disclosures from 2005 to 2014

18. 18 | Follow the Data
Portable device loss
Hacking or malware
Unintended disclosure
Insider leak
Physical loss
Stationary device loss
Payment card fraud
Unknown
26.8%
21.5%
15.7%
12.3%
10.9%
4.6%
2.6%
5.6%
50
25
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Loss or theft
Hacking or
malware
Insider leak
Figure 22: Breach methods observed in the financial industry
There was a fairly even distribution of incidents involving loss or theft, hacking or malware, insider threats,
and unintended disclosures in the financial industry. Perpetrators employ any means to compromise
financial data instead of relying on one or two proven breach methods.
Figure 23: Top 3 breach methods observed in the financial industry from 2005 to 2014
The top 3 breach methods used in the financial industry were loss or theft, hacking or malware attacks,
and insider threats. The reported incident numbers show a decline in loss or theft, which suggests that
strong policies, protocols, and procedures may have been put in place, helping reduce such incidents.
Hacking or malware attacks and insider threats increased.

19. 19 | Follow the Data
Financial + PII
PII
Credentials + financial + PII
Financial
Credentials + PII
Others
27.8%
26.8%
9.2%
7.2%
3.6%
25.4%
40%
20%
0
Hacking or malware
Insider leak
Loss or theft
Unintended disclosure
Payment card fraud
Unknown
PII Health Financial Payment
card
Education Credentials Unknown Others
Figure 24: Record types compromised in the financial industry
The most popular record types and record-type combinations compromised were financial data and
PII; PII; credentials, financial data, and PII; credentials and PII; and financial data. The credentials stolen
usually included online banking log-in details.
Figure 25: Probability of compromising different record types
per breach method in the financial industry
PII and financial data had the highest probability of getting compromised. The most likely data breach
methods were loss or theft and hacking or malware attacks. There is a small chance that credentials and
payment card data will also get compromised.

20. 20 | Follow the Data
120
60
0
20142005 2006 2007 2008 2009 2010 2011 2012 2013
Hacking or malware
Unintended disclosure
Portable device loss
Physical loss
Stationary device loss
Insider leak
Payment card fraud
Unknown
34.2%
28.9%
17.8%
7.2%
6.4%
3.5%
0.1%
1.9%
Data breaches in the education sector
The data breach incident numbers in the education sector have been declining over the years. This could
possibly be due to a shift in focus to more lucrative targets like the healthcare and retail industries.
Figure 26: Education sector data breach incident disclosures from 2005 to 2014
Figure 27: Breach methods observed in the education sector
Hacking or malware attacks, unintended disclosures, and loss or theft accounted for 94.5% of all of the
breaches observed in the education sector.

21. 21 | Follow the Data
50
25
0
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Hacking or
malware
Loss or theft
Unintended
disclosure
PII
Education + PII
Education
Financial + PII
Credentials + PII
Others
47.6%
21.7%
5.5%
3.9%
2.8%
18.5%
Figure 28: Top 3 breach methods observed in the education sector from 2005 to 2014
The top 3 breach methods seen were hacking or malware attacks, loss or theft, and unintended disclosures.
The trend lines for all three methods show a downward pattern.
Figure 29: Record types compromised in the education sector
The most popular record types and record-type combinations compromised were PII as well as education
and PII. Financial data was also compromised in some incidents.

22. 22 | Follow the Data
30%
15%
0
Hacking or malware
Insider leak
Loss or theft
Unintended disclosure
Payment card fraud
Unknown
PII Health Financial Payment
card
Education Credentials Unknown Others
Figure 30: Probability of compromising different record types
per breach method in the education sector
Education and PII posted the highest probability of getting compromised via methods such as hacking or
malware attacks, loss or theft, and unintended disclosures.

23. 23 | Follow the Data
References
1. Privacy Rights Clearinghouse. (2015). About the Privacy Rights Clearinghouse. Last accessed on 3 July 2015,
https://www.privacyrights.org/content/about-privacy-rights-clearinghouse.
2. Privacy Rights Clearinghouse. (2015). “Chronology of Data Breaches Security Breaches 2005–Present.” Last
accessed on 23 June 2015, https://www.privacyrights.org/data-breach.
3. KH Coder [Computer Software]. (2015). Retrieved from http://khc.sourceforge.net/en/.
4. Microsoft Bayesian Network Editor [Computer Software]. (2010). Retrieved from http://research.microsoft.com/
en-us/um/redmond/groups/adapt/msbnx/.
5. Explore Analytics [Online Software]. (2015). Retrieved from https://www.exploreanalytics.com/.
6. Kim Zetter. (29 December 2009). Wired. “Albert Gonzalez Pleads Guilty in Heartland, 7-11 Breaches—Updated.”
Last accessed on 5 July 2015, http://www.wired.com/2009/12/heartland-guilty-plea/.
7. Gordon Kelly. (21 May 2014). Forbes. “eBay Suffers Massive Security Breach, All Users Must Change Their
Passwords.” Last accessed on 5 July 2015, http://www.forbes.com/sites/gordonkelly/2014/05/21/ebay-suffers-
massive-security-breach-all-users-must-their-change-passwords/.
8. Caroline Humer. (24 February 2015). Reuters. “Anthem Says Hack May Affect More Than 8.8 Million Other BCBS
Members.” Last accessed on 5 July 2015, http://www.reuters.com/article/2015/02/25/us-anthem-cybersecurity-
idUSKBN0LS2CS20150225.
9. Numaan Huq. (September 2014). Trend Micro Security Intelligence. “PoS RAM Scraper Malware: Past, Present,
and Future.” Last accessed on 6 July 2015, http://www.trendmicro.com/cloud-content/us/pdfs/security-
intelligence/white-papers/wp-pos-ram-scraper-malware.pdf.
10. Numaan Huq. (March 2015). Trend Micro Security Intelligence. “Defending Against PoS RAM Scrapers: Current
and Next-Generation Technologies.” Last accessed on 6 July 2015, http://www.trendmicro.com/cloud-content/
us/pdfs/security-intelligence/white-papers/wp-defending-against-pos-ram-scrapers.pdf.

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